Citations

Despite the increasing prevalence and median severity of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), medical education on the disease is scant, leading to a diagnostic crisis in which the majority of people with ME/CFS are undiagnosed. We created a care process algorithm in AskMayoExpert accessible to all Mayo Clinic medical providers as a source for information on diagnosis and management of ME/CFS. To evaluate whether the algorithm was associated with improved diagnostic accuracy, we compared concordance before versus after the algorithm was introduced, where concordance was defined as agreement between an appropriately coded referral to Mayo Clinic’s Chronic Fatigue Specialty Clinic and the specialty clinic with an expert diagnosis of ME/CFS. Referrals to the Chronic Fatigue Specialty Clinic increased overall and were more likely to show concordance between specialist diagnosis and referral after the introduction of the ME/CFS AskMayoExpert algorithm. Particularly in diseases that are prevalent and poorly understood, a point-of-care clinical tool may offer just-in-time opportunities to improve diagnosis and management.

Summary Frailty is independently associated with significantly higher 1‐year mortality and MACCE following MTEER. Frail patients also demonstrated increased risks of HF exacerbation, major bleeding, and readmission. Incorporating frailty assessment into pre‐procedural evaluation may improve risk stratification and guide personalized management strategies.

ABSTRACT Importance Rural-urban disparities in chronic disease prevalence are well established; however, the extent to which individual-level socioeconomic status (SES) contributes to these disparities remains unclear. Objective To examine the associations of rurality and SES with the prevalence of five most burdensome chronic diseases among adults. Design We conducted a retrospective cross-sectional study of adults across 27 Upper Midwest counties using the Expanded Rochester Epidemiology Project (E-REP) medical record data linkage system to evaluate associations between rurality, SES and chronic disease prevalence. Prevalence of clinically diagnosed asthma, diabetes, hypertension, coronary heart disease, and mood disorders was identified from International Classification of Diseases ICD9/10 codes over a five-year period (2014-2019). Setting Population based Participants Adults over 18 years residing in the 27 E-REP counties, excluding those missing rural-urban residence status. Exposure HOUSES index, an individual-level measure of SES, served as the primary measure, while rurality based on Rural Urban Commuting Area (RUCA) codes 4-10 was the secondary measure. Main Outcome Prevalence of the five clinically diagnosed chronic diseases was identified using ICD9/10 codes from 2014–2019. Mixed effect logistic regression models were used and adjusted for demographics and general medical examination receipt, to assess rural-urban and SES differences for prevalence of each chronic disease. Results Among 455,802 adults with available HOUSES index, 42.8% lived in rural areas, 53.8% were female and 87.4% were non-Hispanic White. In the unadjusted analysis, rural and urban populations showed comparable asthma and CHD prevalence, while mood disorders, hypertension, and diabetes were more common in urban areas. After adjusting for demographic factors and healthcare utilization, rural-urban differences were no longer statistically significant, whereas SES remained strongly associated with all diseases in a dose response manner (e.g., adjusted Odds Ratio for hypertension (ref: HOUSES index Q4): 1.14, 1.27, and 1.42 for HOUSES index Q3, Q2, and Q1, respectively). Conclusions and Relevance Individual-level SES measured by the HOUSES index, was more strongly associated with chronic disease prevalence than rurality, supporting its integration into population health assessment and risk stratification.

Acute type A aortic dissection is a life-threatening condition requiring emergency surgery and complex postoperative management. Although survival rates have improved, many patients experience long-term functional impairments, reduced quality of life, and an elevated risk of complications. Despite strong evidence supporting cardiac rehabilitation in other cardiovascular populations, structured programs remain underutilized in patients with surgically resolved acute type A aortic dissection. Exercise-based cardiac rehabilitation appears feasible and can be delivered safely in carefully selected patients when appropriately adapted to individual needs and conducted under close supervision. Postoperative patients are often physically deconditioned, prone to hospital-acquired disability, and may misjudge exercise intensity. Therefore, individualized exercise prescription, guided by exercise testing when available, is important to support safe training thresholds. Early and gradual introduction of physical activity may help prevent complications associated with immobility, support blood pressure control, and contribute to improvements in functional capacity. However, training volume should be purposefully lower than in conventional program settings to reduce hemodynamic stress. Education on safe exercise parameters and self-monitoring plays a central role in enabling long-term adherence and promoting patient autonomy. Cardiac rehabilitation programs should incorporate dietary, nutritional, and psychological support. Although evidence specific to this patient population remains limited, available data suggest the feasibility and potential benefits of cardiac rehabilitation when delivered with appropriate precautions. Our review underscores the need for a tailored, multidisciplinary CR approach aimed at enhancing physical recovery, supporting cardiovascular stability, and improving overall quality of life in patients following surgery. Further research is required to define optimal program protocols.

Homeobox (HOX) transcription factors are encoded within highly organized loci expressed along an anterior–posterior axis through embryogenesis and in a pleiotropic manner in hematopoiesis. HOX expression has been exhaustively studied in the context of oncogenesis and malignancy, but the compensatory substitution of HOX paralogs makes mechanistic annotation in steady-state hematopoiesis challenging. Despite this, HOX genes reflect numerous non-redundant roles in healthy hematopoiesis including HSC self-renewal, development, lymphopoiesis, myelopoiesis, and erythropoiesis. Here, we review historical and current insights into HOX functions in steady-state hematopoiesis and highlight unexplored avenues in their biology that could further elucidate their significance to hematopoietic homeostasis.

Management of unstable upper lumbar fractures with corpectomy and posterior fixation is technically demanding, and conventional workflows may require intraoperative repositioning, increasing operative complexity. Lateral mini-open upper lumbar corpectomy (LMULC) paired with robotic-assisted (RA) posterior percutaneous pedicle screw fixation (PPPSF) can be performed in a single position to facilitate ventral spinal decompression and stabilization in the anatomically constrained upper lumbar spine. In this study, we describe the operative technique and report four illustrative cases of unstable L1 or L2 fractures treated with single-position LMULC, RA-PPPSF, and short-segment fusion. Clinical, radiological, intraoperative variables and postoperative outcomes were evaluated. The mean age was 52.3 ± 17.7 years. The median operation time was 314 min (range 268–361 min); the median estimated blood loss (EBL) was 225 mL (range 100–400 mL). The median preoperative kyphosis was 10.15° (range 8.4–14.6°), the median postoperative kyphosis measured 6.65° (range 1.7–10.8°) and the median correction achieved was 3.5° (range −2.4–12.9°). The median visual analog scale (VAS) pain score reduced from 7 (range 7–9) preoperatively to 4.5 (range 2–6) postoperatively at discharge. At a median follow-up of 12 months (range 6–15 months), all patients had uncomplicated recoveries, demonstrated solid fusion on imaging, and reported favorable MacNab outcomes. Single-position LMULC with RA-PPPSF was technically feasible in this preliminary illustrative series and resulted in favorable clinical and radiographic outcomes. However, further studies in larger cohorts are warranted to help confirm these findings and better define the potential advantages and limitations of this technique.

Background/Objectives: Machine learning (ML) models can predict hospital admission from emergency department (ED) triage data with areas under the receiver operating characteristic curve (AUC) exceeding 0.85. Whether incorporating the assigned provider’s identity—as a proxy for unmeasured practice variation—improves prediction has not been systematically studied. We aimed to compare 10 supervised ML classifiers for predicting hospital admission at ED triage, with and without provider identity, and to characterize model reasoning using SHapley Additive exPlanations (SHAP). Methods: We conducted a retrospective cohort study of 186,094 ED visits (2020–2023, training) and 58,151 visits (2024, temporal holdout test) at one academic tertiary-care ED. Ten classifiers spanning linear, distance-based, tree-based, ensemble, probabilistic, and neural network families were each trained in two conditions: baseline (23 triage features) and with provider identity appended. SHAP TreeExplainer was applied to the top-performing models (CatBoost and XGBoost). Results: The admission rate was 31.3% (training) and 31.7% (test). CatBoost achieved the highest baseline AUC of 0.8906 (0.8878–0.8933). Adding provider identity produced negligible AUC changes across all models (ΔAUC range: −0.0029 to +0.0015; all DeLong p > 0.05). SHAP analysis identified ESI level, respiratory rate, temperature, complaint category, and age as the dominant predictors, with clinically intuitive directionality. Conclusions: Provider identity does not meaningfully improve ML prediction of hospital admission beyond standard triage variables. The observed 28-percentage-point variation in provider admission rates is explained by patient case-mix differences than with independent practice pattern effects on prediction. SHAP provides transparent, clinically interpretable explanations suitable for bedside decision support.

Objective Gain‐of‐function variants in IDH1 and IDH2 are enriched among individuals with coexisting myeloid neoplasms and autoimmune diseases. However, the association between IDH1 / IDH2 variants and rheumatic diseases independent of hematologic neoplasms remains unclear. We investigated the association between rare gain‐of‐function and loss‐of‐function genetic variants in IDH1 and IDH2 and immune‐mediated rheumatic diseases. Methods Individuals with gain‐of‐function and loss‐of‐function variants in IDH1/IDH2 were identified by whole genome sequencing in the UK Biobank and the All of Us Research Program. These individuals were matched 1:10 to controls without IDH1 or IDH2 variants by age, sex, and ancestry. The frequency of immune‐mediated rheumatic diseases in the presence and absence of IDH1/IDH2 variants was compared using chi‐square or Fisher exact tests. Results Fifty‐five individuals with IDH1 or IDH2 gain‐of‐function variants without pre‐existing hematologic neoplasms were identified in the UK Biobank. These individuals had a significantly higher frequency of incident immune‐mediated rheumatic diseases compared to matched controls (11 of 55 [20%] vs 20 of 550 [4%], odds ratio [OR] = 6.6, P = 2.7 × 10 −5 ), mainly polymyalgia rheumatica (6 of 11) and rheumatoid arthritis (5 of 11). Replication in the All of Us cohort identified 65 individuals with gain‐of‐function variants and confirmed an increased odds of incident immune‐mediated rheumatic diseases compared to controls (OR = 7.7, P < 5 × 10 −4 ), predominantly rheumatoid arthritis. There was no association between loss‐of‐function variants in IDH1/IDH2 and immune‐mediated rheumatic diseases in either cohort. Conclusion Rare gain‐of‐function variants in IDH1 / IDH2 are associated with increased risk of immune‐mediated rheumatic diseases, particularly rheumatoid arthritis and polymyalgia rheumatica. image

Abstract Clinical narrative text contains crucial patient information, yet reliable extraction remains challenging due to linguistic variability, documentation habits, and differences across care settings. Large language models (LLMs) have shown strong accuracy on clinical information extraction (IE), but their reproducibility (stability under repeated runs) and robustness (stability under small, natural prompt variations) are less consistently quantified, despite being central to clinical deployment. In this study, we evaluate three open-weight LLMs representing distinct modeling choices: a dense general-purpose model (Llama 3.3), a mixture-of-experts (MoE) general-purpose model (Llama 4), and a domain-tuned medical model (MedGemma). We focus on binary clinical IE aligned with four mobility classes from the International Classification of Functioning, Disability and Health (ICF) framework. Using a controlled experimental design, we quantify (1) intra-prompt reproducibility across repeated sampling and (2) inter-prompt robustness across paraphrased prompts. We jointly report predictive performance (F1-score) and stability (Fleiss’ Kappa). And we test factor effects using three-way ANOVA with post-hoc comparisons. Results show that increasing temperature generally degrades agreement, but the magnitude depends on model and task; furthermore, prompt paraphrasing can substantially reduce stability, with particularly large drops for the MoE model. Finally, we evaluate a practical mitigation, self-consistency via majority voting, which improves κ substantially and often improves or preserves F1-score, at the cost of additional inference. Together, these findings provide a reproducible framework and concrete recommendations for evaluating and improving LLM reliability in clinical IE.

Abstract Introduction The evaluation of diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early detection and help prevent progression to heart failure. Methodology A group of 15 diabesity cases with chest x-ray examinations (< 1 month) before and after ≥ 12 months of incretin-based therapy (absent ventricular/valvular dysfunction) were identified. Standard diabesity descriptors and predictions of pulmonary venous hypertension (aka “pulmonary congestion”) [None; Stage 1: vascular distention/redistribution but minimal interstitial edema; or Stage ≥ 2: vascular congestion with ≥ mild interstitial or alveolar edema] by validated AI-enabled chest x-ray staging, representing mean left atrial pressure in diastolic dysfunction, were evaluated pre- and post-therapy. Results By weight loss, cases clustered into equal-sized subgroups: 1. Significant Responders (9–30% decreases); 2. Insignificant Responders (0–2% decreases); and 3. Non-Responders (2–12% increases). Regarding hemoglobin A1c changes: 1. Significant Responders collectively decreased; 2. Insignificant Responders varied; and 3. Non-Responders were largely stable. Pre-therapy, all 15 cases demonstrated AI-enabled chest x-ray evidence of diastolic dysfunction; post-therapy, 4 improved (especially cases of greatest weight loss or hemoglobin A1c reduction), 5 were stable, and 6 worsened. Per subgroup, AI-enabled chest x-ray indications therapeutic response were: 1. Significant Responders (all demonstrating unequivocally decreased obesity and improved diabetes) collectively showed stable-decreased dysfunction; 2. Insignificant Responders (all demonstrating minimally decreased obesity but stable-worsening diabetes in most) reflected stable-increased dysfunction in 60%; and 3. Non-Responders (all demonstrating increased obesity but stable-improved diabetes) showed increased dysfunction in 80%. Conclusion AI-enabled CXR staging detects background subclinical diastolic dysfunction in diabesity and monitors its response to incretin-based therapy.